Energy storage calculation method for new energy power generation consumption

Abstract

The invention discloses an energy storage calculation method for new energy power generation consumption, belongs to the technical field of electrochemical energy storage, and realizes optimal management of an energy storage system by constructing an energy storage system model, accurately updating the health state of a battery and designing a power data generation mode and an efficient new energy consumption processing strategy. According to the method, the influence of the battery health state on the performance and the service life of the system is considered, the battery health state serves as a core variable of energy storage calculation, an effective energy storage arbitrage algorithm and strategy are designed, the electricity price difference economic potential is fully mined, the economic benefit maximization of the energy storage system is achieved, and the overall efficiency of the energy storage system is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of electrochemical energy storage, and relates to the power optimization technology of an electrochemical energy storage power station. Specifically, it relates to a storage calculation method for new energy power generation consumption, which measures the new energy power generation consumption of the battery power of an electrochemical energy storage power station in the electricity spot market, realizes the efficient consumption of new energy power generation, and maximizes the economic benefits of the energy storage system. Background Art

[0002] In today's energy field, the development of new energy has attracted much attention. New energy sources such as solar energy and wind energy have the remarkable advantages of being clean and renewable, but their power generation characteristics also bring a series of challenges. The intermittency and volatility of new energy power generation are the key issues among them. Due to the influence of natural conditions, such as the change of sunlight intensity and the instability of wind speed, the power generation power of new energy shows great instability. When the power generation power is too high, the power grid may not be able to consume all the electricity in time, resulting in the occurrence of curtailment of electricity and causing waste of energy. When the power generation power is insufficient, it is difficult to meet the electricity demand of users, affecting the reliability of power supply. At the same time, as an important means to solve the problem of new energy consumption, the management of the energy storage system itself also faces many difficulties. As the core component of the energy storage system, the rationality of the charging and discharging operations of the battery directly affects the service life and performance of the battery. Unreasonable charging and discharging strategies may lead to accelerated battery aging, not only increasing the maintenance cost, but also possibly reducing the overall efficiency of the energy storage system. In addition, in the electricity market environment, the economic operation of the energy storage system is also a key point that needs attention. There are differences in electricity prices at different times. How to utilize this difference to achieve energy storage arbitrage and improve the economic benefits of the energy storage system is a research direction with practical significance. However, the existing technologies and methods often have the following deficiencies when solving these problems:

[0003] First, the consideration of the battery health state is insufficient, and a reasonable charging and discharging strategy cannot be formulated according to the actual situation of the battery, thus affecting the service life and performance of the battery.

[0004] Second, in terms of energy storage arbitrage, there is a lack of effective algorithms and strategies, and it is difficult to fully exploit the economic potential brought by the electricity price difference, resulting in the inability to maximize the economic benefits of the energy storage system.

[0005] Therefore, there is an urgent need to develop a calculation method for energy storage technology for new energy power generation consumption that can accurately predict new energy power generation, fully consider the battery health status, and have an efficient energy storage arbitrage algorithm. Through such a method, the problems of power curtailment and power supply reliability caused by the intermittency and volatility of new energy power generation can be effectively solved, the charging and discharging strategies of the energy storage system can be reasonably planned, the battery service life can be extended, the maintenance cost can be reduced, and at the same time, the economic potential brought by the electricity price difference can be fully exploited, the economic benefits of the energy storage system can be maximized, the coordinated development of new energy power generation and energy storage technology can be promoted, and the development needs of the energy field and the changes in the power market environment can be better adapted. Summary of the Invention

[0006] In order to overcome the deficiencies in the existing new energy power generation consumption and energy storage system management technologies, such as inaccurate prediction of new energy power generation, insufficient consideration of the battery health status, and lack of effectiveness in the energy storage arbitrage strategy, the present invention provides a calculation method for energy storage for new energy power generation consumption, which can achieve the efficient consumption of new energy power generation and the maximization of the economic benefits of the energy storage system.

[0007] The method of the present invention comprehensively considers factors such as the volatility of new energy power generation, the battery health status, and the electricity price difference. By constructing an energy storage system model, accurately updating the battery health status, designing the generation method of power data, and an efficient new energy consumption processing strategy, the all-round optimized management of the energy storage system is realized. Focus on the impact of the battery health status on the system performance and life, and incorporate it into the core link of energy storage calculation to improve the overall efficiency of the energy storage system.

[0008] To achieve the above object, the technical solution provided by the present invention is as follows:

[0009] A calculation method for energy storage for new energy power generation consumption, comprising the following steps:

[0010] 1) Modeling of the energy storage system, designing the charging and discharging methods of the energy storage system battery:

[0011] Define the energy storage system class variables, covering key parameters such as energy storage capacity, current stored energy, battery charging efficiency, discharging efficiency, battery health status (SOH), and state of charge (SOC).

[0012] According to the inherent characteristics of the energy storage system battery type, capacity, etc., set an appropriate charging and discharging power range to ensure that the battery works within a safe and stable power range, avoiding damage to the battery due to excessive power or affecting the efficiency due to too low power. Incorporate key variables such as battery charging power, charging efficiency, charging time interval, and discharging power, discharging efficiency, discharging time interval into the calculation, design the charging and discharging methods, achieve efficient and reasonable charging and discharging operations, and update the SOC in real time.

[0013] In terms of the design of the charge-discharge method, the present invention breaks through the traditional method in the energy storage system modeling. For the first time, the state of health (SOH) of the battery is incorporated into the model as the core dynamic variable. By establishing a linear aging relationship between SOH and the charge-discharge depth (charge aging coefficient k_charge and discharge aging coefficient k_discharge), the real-time quantitative assessment of the battery life is realized. The model adopts a hierarchical architecture design: the basic parameter layer defines the inherent attributes such as energy storage capacity, efficiency, initial SOC / SOH, etc.; the constraint control layer sets the upper limit of the charge-discharge power (for example, the maximum charge-discharge power is 80% of the capacity) to ensure that the battery operates within a safe range; the dynamic update layer calculates the changes in SOC and SOH in real time through the charge / discharge method to form a closed loop. This design enables the system to dynamically adjust the charge-discharge strategy according to the battery state of health, significantly extending the battery life, while the traditional method only estimates the life through a fixed number of cycles and cannot adapt to the non-linear aging characteristics in actual operation.

[0014] The charging process follows a closed-loop process of power-limited - energy calculation - SOC update - SOH correction, including: the actual charging power does not exceed the maximum allowable power of the battery (such as 80% of the capacity). Based on the charging efficiency, the actual stored energy is calculated and converted into a SOC percentage. At the same time, according to the charge depth, the SOH is subtracted proportionally to simulate aging. The discharging process realizes efficient management through power constraint, energy accounting, SOC monitoring, and SOH correction: the discharging power does not exceed the maximum discharging capacity. Considering the efficiency loss, the output energy is accurately calculated to ensure that the SOC is not lower than the minimum threshold (such as 5%), and the SOH is dynamically updated according to the discharge depth to reflect the different impacts of different discharge depths on the life.

[0015] 2) Create a method for updating the state of health of the battery to update the state of health of the battery:

[0016] Based on the correlation between the charge-discharge depth of the battery and the change in SOH, a specific linear relationship model is constructed to simulate battery aging. Create a method for updating the state of health of the battery. According to the charge-discharge depth, the constructed linear relationship model is used to simulate the aging process to ensure that the SOH is constrained between 0 and 1.

[0017] 3) Data generation to obtain power generation power data, charge-discharge power data, new energy power generation data, and electricity price data:

[0018] New energy power generation data (currently existing power generation power data and charge-discharge power data), combined with a sine function and random noise to generate new energy power generation data, ensuring that the power generation power is positive.

[0019] Using the existing electricity price data generation function, a day is divided into three periods: peak, flat, and valley. A reasonable electricity price range is set and specific electricity price data is generated.

[0020] 4) Design a new energy consumption and handling method, adopting a segmented control method:

[0021] This new energy consumption and handling adopts a segmented control strategy, aiming to efficiently reduce the phenomenon of abandoned electricity and improve the utilization efficiency of new energy. This strategy covers processing flows with different priorities to ensure maximum consumption of new energy power under various circumstances.

[0022] Basis of the consumption strategy: The new energy consumption and handling function adopts the strategy of preferentially storing excess electricity in the energy storage system until it reaches its maximum capacity, reducing the phenomenon of abandoned electricity. The new energy consumption and handling function uses the method of a piecewise function. At each time step, this function will monitor the power generation of new energy in real time and compare it with the current electricity demand. If it is detected that the new energy power generation exceeds the current electricity demand, then this excess electricity will be preferentially stored in the energy storage system. During the process of electricity storage, the state of charge (SOC) of the energy storage system will be continuously tracked and updated. As the excess electricity is continuously stored, the SOC will gradually increase.

[0023] When the SOC of the energy storage system reaches its maximum capacity, it means that the energy storage system can no longer store more electricity. If there is still excess power generation, appropriate incentives are used to encourage users to increase their electricity consumption load during this period to reduce the occurrence of abandoned electricity.

[0024] Three-level priority control: The consumption strategy proposed in this invention adopts three-level priority control. The first level is that when there is excess power generation, electricity is preferentially stored in the energy storage until the SOC reaches 95% (reserving 5% safety capacity). The second level is that when the energy storage system reaches the upper limit of 95% SOC, the dynamic electricity price lever is used to guide users to increase their electricity consumption load. Using the price mechanism, users are incentivized to consume more electricity during the period of excess new energy power generation, thereby further consuming the excess power. The third level is that if there is still a surplus, cross-regional consumption is achieved through V2G (Vehicle-to-Grid) or virtual power plants. Compared with traditional methods, this strategy realizes innovation through dual-constraint optimization (maximizing the consumption volume and SOH constraint), dynamic electricity price response (charging in valleys and discharging in peaks), and multi-time scale coordination (combining ultra-short-term and short-term forecasts).

[0025] Energy storage arbitrage and economic evaluation: Design a dynamic programming model to solve the function. According to the electricity price and the state of the energy storage system, calculate the benefits that can be obtained from charging or discharging at different time points, and by continuously adjusting the charge-discharge strategy, find a solution that maximizes the energy storage arbitrage profit. For example, charging during the electricity price valley and discharging during the electricity price peak, conducting energy storage arbitrage operations and economic evaluations to maximize the economic benefits of the energy storage system.

[0026] The present invention realizes the optimization of the energy storage arbitrage strategy by constructing a dynamic programming model. In specific implementation, first, a day is divided into time intervals of Δt (such as 15 minutes), and the state at each time step t consists of the state of charge SOC(s t ), the state of health of the battery SOH(h t ), and the electricity price forecast (p t ). The objective function of the dynamic programming model is defined as maximizing the total profit, and the expression for the total profit Profit is:

[0027]

[0028] Among them, in the formula system for optimizing the energy storage arbitrage strategy of the present invention, T represents the total number of time steps after dividing a day into time intervals of Δt; is the discharge electricity price at time step t, is the discharge electricity quantity at time step t, is the charging electricity price at time step t, is the charging electricity quantity at time step t, η charge and η discharge represent the charging efficiency and the discharge efficiency respectively; C aging is the battery aging cost, which is related to the aging coefficient of the charge-discharge depth SOH (C aging = k_charge / k_discharge).

[0029] The goal of this system is to maximize the total profit. The Bellman equation decomposes a complex decision-making problem (i.e., the charge-discharge decision at each time step throughout the day) into a series of simple sub-problems in a recursive manner. The strategy optimization process is specifically based on the Bellman equation:

[0030]

[0031] In the formula, γ is the discount factor, R t is the immediate profit at the current time step, and a t is the power of the charge-discharge action (including the charging power, the discharging power, or idle). Based on the Bellman equation, the optimal benefit in each state is calculated step by step.

[0032] In the process of solving the optimal strategy, it is necessary to update the state according to the state transition equation, and then calculate the optimal value in different states. That is, the value of V t+1 needs to be obtained from the state transition equation, and the state transition equation is:

[0033]

[0034] Among them, V t (s t ,h t) is the optimal value function V at time step t with state s t (including state of charge SOC and state of health SOH of the battery), t+1 (s t+1 , h t+1 ) has a similar meaning, but corresponds to time step t + 1 and the updated state. s t+1 and h t+1 are the state of charge SOC and state of health SOH of the battery at time step t + 1 respectively; capacity is the capacity of the energy storage system; and are the charging power and discharging power at time step t; η charge and η discharge represent the charging efficiency and discharging efficiency respectively.

[0035] By updating the value iteration to maximize the total profit of each state, an optimal charging and discharging sequence is finally generated. Compared with the traditional static strategy, this method realizes the coordinated optimization of arbitrage income and battery life through the electricity price generation function. For example, when the peak-valley difference of the electricity price is significant and the battery SOH is low, the shallow charge and discharge strategy is preferentially selected to extend the life, rather than simply pursuing the maximization of short-term income.

[0036] Compared with the existing technology, the beneficial effects of the present invention include:

[0037] The present invention provides an energy storage calculation method for new energy power generation accommodation. Through the modeling of the energy storage system, the charging and discharging strategies of the energy storage system are designed (that is, the charging and discharging methods are designed), which has technical advantages for the dynamic optimization and life management of the energy storage system, including: First, fully considering the state of health of the battery, formulating reasonable charging and discharging strategies, and extending the service life and improving the performance of the battery. Second, having an effective energy storage arbitrage algorithm and strategy, fully exploring the economic potential of electricity price differences, and maximizing the economic benefits of the energy storage system. Brief Description of the Drawings

[0038] Figure 1 is a flow chart of the method of the present invention. Detailed Embodiments

[0039] The following will describe in detail the specific embodiments of the present invention with reference to the drawings. The specific implementation steps are as follows:

[0040] The energy storage calculation method for new energy power generation accommodation provided by the present invention constructs an energy storage system model to clarify the key parameters and operation logic of the energy storage system; establishes a battery state of health update mechanism to accurately control the battery aging situation; uses the existing data generation method to simulate the real new energy power generation power change; implements an efficient new energy accommodation processing strategy to maximize the economic benefits of the energy storage system.

[0041] The present invention generates new energy power generation data using a hybrid model that combines a sine function and random noise. First, according to the statistical laws of historical data of the power generation type (such as solar / wind energy), the period (such as 24 hours), amplitude (such as 80% of the maximum power generation) and phase shift parameters of the sine function are determined to simulate the periodic change trend of the power generation. On this basis, random noise conforming to a normal distribution (the standard deviation is determined according to the fluctuation range of historical data) is superimposed to reflect the uncertainty in the actual power generation process. To ensure the physical rationality of the generated data, truncation processing is used to ensure that the power generation is always positive, and the extreme value interference is eliminated by the sliding window smoothing algorithm. Compared with the traditional single sine wave or random generation method, the data generation method adopted by the present invention can more realistically reproduce the volatility and regularity characteristics of new energy power generation.

[0042] Energy storage system modeling (energy storage system modeling module)

[0043] Parameter setting: According to different application scenarios, the energy storage capacity is accurately set. The initial value of the currently stored energy is initialized according to the actual situation, and the charge efficiency and discharge efficiency are determined according to the selected battery type and technical specifications. At the same time, the initial values of the state of health (SOH) and state of charge (SOC) of the battery are carefully considered to ensure that the true initial condition of the battery is reflected truthfully.

[0044] Charge and discharge method: In the charging method, when performing the charging operation, the input charging power is fully considered, and the actually increased stored energy is calculated according to the charging efficiency, and the SOC is updated synchronously. The calculation formula is:

[0045] ΔE charge =P charge ×η charge ×Δt

[0046] , where ΔEcharge is the actually increased stored energy, P charge is the charging power, η charge is the charging efficiency, and Δt is the charging time interval. The discharging process is processed in the discharging method. Similarly, the influence of the discharging efficiency on the output energy and the corresponding change of the SOC are considered. The calculation formula is:

[0047] ΔE discharge =P discharge ×η discharge ×Δt

[0048] , where ΔE discharge is the actually output energy, P discharge is the discharging power, and η discharge is the discharging efficiency.

[0049] Next, dynamic adjustment is carried out in combination with external factors such as the volatility of new energy power generation and electricity price differences. During periods when new energy power generation is excessive and electricity prices are low, increase the charging power to fully store the excess electrical energy; while during peak electricity consumption periods and when electricity prices are high, appropriately increase the discharging power to achieve energy storage arbitrage, thereby improving the economic benefits of the energy storage system. By updating the SOC in real time, the energy state of the energy storage system can be accurately grasped in a timely manner.

[0050] Battery health state update

[0051] Update method: There is a close connection between the method of updating the battery health state and the depth of charge and discharge. When the charging depth is large, reduce the SOH at a specific linear ratio to simulate the accelerated aging of the battery. The method of updating the battery health state is expressed as a calculation formula:

[0052] ΔSOH charge =-kcharge×ΔDOD charge

[0053] Among them, ΔSOH charge is the change in SOH caused by charging, kcharge is the charging aging coefficient, and ΔDOD charge is the change in the depth of charge. In the case of battery discharge, the method of updating the battery health state is expressed as a calculation formula:

[0054] ΔSOH discharge =-kdischarge×ΔDOD discharge ,

[0055] Among them, ΔSOH discharge is the change in SOH caused by discharge, kdischarge is the discharge aging coefficient, and ΔDOD discharge is the change in the depth of discharge. Through this precise control, the effective service life of the battery is effectively extended.

[0056] Data generation (new energy power generation data module)

[0057] New energy power generation data generation: The existing new energy power generation data generation function uses the sine function to simulate the periodic change trend of new energy power generation, and at the same time adds random noise to reflect the uncertainty and volatility in reality. The minimum value of the power generation is strictly limited to zero to conform to physical reality. In the specific generation process, according to the characteristics of different new energy power generation types (such as solar energy, wind energy), the parameters of the sine function and the range of random noise can be adjusted to ensure that the generated data is more in line with the actual power generation situation.

[0058] The sine function used in the new energy power generation data generation function has its parameters such as period and amplitude adjusted based on the analysis of long-term new energy power generation data. For example, for solar power generation, its power shows an obvious periodicity within a day, gradually rising from early morning, reaching the peak at noon, and then gradually decreasing. By adjusting the period of the sine function to 24 hours and setting an appropriate amplitude, this changing trend can be simulated. For the random noise part, it is usually generated through a normal distribution, and the selection of the standard deviation depends on the statistical analysis of the fluctuation degree of historical power generation data, so as to simulate the unpredictable power changes in the real scenario.

[0059] Electricity price data generation: When the existing electricity price data generation function divides peak, flat, and valley periods, it fully refers to the historical data and policy regulations of the local power market. The setting of the electricity price range takes into account both cost factors and leaves a reasonable profit margin for arbitrage operations. For example, during peak hours, the electricity price is set relatively high to reflect the peak of electricity demand and the tight supply; during valley hours, the electricity price is set low to encourage users to consume electricity at this time and also provide an economical opportunity for the charging of energy storage systems.

[0060] From the perspective of practical application, when processing historical data, the electricity price data generation function identifies the time node rules of peak and valley electricity consumption and the electricity price difference patterns under different seasons, weekdays, and weekends through time series analysis of multi-year electricity price data. In terms of combining policy regulations, if the local policy has a subsidy policy for new energy consumption, the function will incorporate the subsidy factor into the calculation of valley electricity price, so that while encouraging energy storage charging, it can also promote the consumption of new energy electricity. Moreover, the function will dynamically adjust the electricity price range according to the real-time supply and demand feedback of the power market to more accurately reflect the market situation.

[0061] New energy consumption processing

[0062] Consumption strategy: The strategy for new energy consumption processing is to make real-time judgments on the new energy power generation power for each time step. If the power generation power exceeds the current demand, the excess electricity will be preferentially stored in the energy storage system until its maximum capacity is reached. During this process, the change of SOC is continuously tracked and updated to ensure the safety and stability of system operation. When the energy storage system reaches its maximum capacity, if there is still excess power generation, other consumption methods can be considered, such as negotiating with surrounding users to increase the electricity load or participating in the peak shaving auxiliary services of the power grid.

[0063] The new energy consumption strategy needs to work in coordination with the energy storage system modeling module and the new energy power generation data module. When charging the energy storage system, it closely cooperates with the energy storage system management module, and accurately controls the charging rate and time according to the battery characteristics and charging efficiency curve to prevent problems such as battery overheating and overcharging. During the whole process, it also records and analyzes various data in the consumption process, such as the number of charge and discharge cycles, the change of curtailed power, etc., providing a basis for optimizing the consumption strategy in the future.

[0064] The formula for obtaining the excess power (curtailed power) is expressed as:

[0065] E surplus = max(0, P gen - P demand ) × Δt

[0066] In the above formula, E surplus represents the excess power, and its value is jointly determined by the difference between the new energy power generation P gen and the current power demand P demand and the time step Δt. The calculation step of max(0, P gen - P demand ) is to ensure that the excess power is calculated only when the power generation is greater than the demand power. If the power generation is less than or equal to the demand power, the excess power is 0. Multiplying this difference by the time step Δt can obtain the excess power generated during this time step.

[0067] Energy storage arbitrage and economic evaluation: Call the dynamic programming model to solve the function for energy storage arbitrage operation and economic evaluation. This function comprehensively considers multiple factors such as electricity price changes, charge and discharge costs, and battery aging costs. Through complex calculation and optimization algorithms, it determines the optimal charge and discharge timing to maximize the energy storage arbitrage profit. At the same time, it also comprehensively evaluates the operating costs and revenues of the entire energy storage system to provide strong support for decision-making. During the evaluation process, multiple evaluation indicators can be used, such as the internal rate of return (IRR), net present value (NPV), etc., to comprehensively measure the economic benefits of the energy storage system.

[0068] The optimization algorithm for this function is the dynamic programming algorithm or the genetic algorithm, etc. In specific implementation, we adopted the dynamic programming algorithm, which divides the entire time span into multiple stages. At each stage, based on factors such as the current electricity price, energy storage state, cost, etc., it calculates the optimal charge and discharge decisions, and finds the globally optimal charge and discharge strategy through step-by-step recursion. When calculating the battery aging cost, the function establishes an aging cost calculation model related to the charge and discharge depth and number of cycles based on the equivalent circuit model and cycle life curve of the battery. Moreover, when evaluating economic indicators, the function takes into account the time value of money, and by reasonably selecting the discount rate, the calculated IRR and NPV can better reflect the true economic value of the project, providing a scientific basis for long-term investment decisions.

[0069] When using the dynamic programming algorithm to solve the optimal strategy, the calculation efficiency is improved through the following steps:

[0070] State space discretization: Divide SOC and SOH into 100 discrete levels to reduce the calculation dimension;

[0071] Value function iteration: Based on the Bellman equation, calculate the optimal benefit at each state for each time period:

[0072] V t (s) = max a∈A [R(s,a) + γV t+1 (s′)]

[0073] Pruning optimization: Prematurely terminate invalid states (such as SOC = 0 and unable to charge) to reduce the calculation complexity.

[0074] The present invention is further illustrated by the following examples.

[0075] Example: Build a new energy storage project in a certain area, where the solar energy resources in this area are rich and the peak-valley electricity price difference in the electricity market is obvious.

[0076] Characteristics of the energy storage system:

[0077] Use ternary lithium batteries with an energy storage capacity of X megawatt-hours.

[0078] The initial charge efficiency is 92% and the discharge efficiency is 90%.

[0079] The self-discharge rate of the battery is 0.2% per day.

[0080] Battery life characteristics:

[0081] At 80% charge and discharge depth, after 1500 cycles, the capacity retention rate is about 80%, and at this time, the physical life of the battery is considered to end.

[0082] The average calendar aging rate is 1.5% capacity loss per year.

[0083] Data generation:

[0084] The new - energy power generation data is generated according to the historical solar irradiance data of the region, using the sine function and random noise.

[0085] The electricity price data is divided according to the peak - valley periods of the local power market. The electricity price during the peak period is X1 yuan per kilowatt - hour, the electricity price during the flat period is X2 yuan per kilowatt - hour, and the electricity price during the valley period is X3 yuan per kilowatt - hour.

[0086] New - energy accommodation processing:

[0087] The new - energy accommodation processing function adopts the strategy of preferentially storing the excess power into the energy storage system until it reaches the maximum capacity, reducing the phenomenon of abandoned electricity. The new - energy accommodation processing function uses a piece - wise function method. At each time step, the function will monitor the power generation of new energy in real - time and compare it with the current power demand. If it is detected that the new - energy power generation exceeds the current power demand, then these excess powers will be preferentially stored in the energy storage system. During the process of power storage, the state of charge (SOC) of the energy storage system will be continuously tracked and updated. As the excess power is continuously stored, the SOC will gradually increase. When the SOC of the energy storage system reaches its maximum capacity, it means that the energy storage system can no longer store more power. If there is still excess power generation, appropriate incentive measures are taken to encourage users to increase their electricity load during this period, reducing the occurrence of abandoned - electricity phenomenon.

[0088] On a certain day, through the new - energy accommodation processing function, the excess solar power generation was successfully stored in the energy storage system, reducing the phenomenon of abandoned electricity.

[0089] Through the calculation of the dynamic programming model solution function, the optimal charging and discharging timing was determined, realizing the maximization of the energy - storage arbitrage profit. By analyzing the actual operation data of this project, the effectiveness of the method of the present invention in terms of new - energy power generation accommodation and improvement of the economic benefits of the energy storage system was verified.

[0090] It should be noted that the purpose of publishing the embodiments is to help further understand the present invention. However, those skilled in the art can understand that within the scope not departing from the present invention and the appended claims, various substitutions and modifications are possible. Therefore, the present invention should not be limited to the content disclosed in the embodiments, and the scope of protection claimed by the present invention shall be subject to the scope defined by the claims.

Claims

1. A storage calculation method for new energy power generation and consumption, characterized in that, Optimize the energy storage system by constructing an energy storage system model, accurately updating the battery health state, designing the generation method of power data, and an efficient new energy consumption processing strategy, including the following steps: 1) Construct an energy storage system model, including defining energy storage system class variables and designing the charging and discharging methods of the energy storage system battery, namely the charge and discharge strategy: Take the battery health state as the core dynamic variable and realize the real-time quantitative evaluation of the battery life by establishing a linear aging relationship between the battery health state and the charge and discharge depth; The energy storage system model adopts a hierarchical architecture design, including a basic parameter layer, a constraint control layer, and a dynamic update layer; the basic parameter layer is used to define energy storage system class variables, including energy storage capacity, efficiency, initial state of charge, and battery health state; the constraint control layer is used to set the upper limit of charge and discharge power; the dynamic update layer calculates the changes in the state of charge and battery health state in real time through the charging or discharging method to form a closed-loop process; 2) Create a battery health state update method to update the battery health state: Based on the relationship between the battery charge and discharge depth and the change in the battery health state, construct a linear relationship model to simulate battery aging, create a battery health state update method, and use the constructed linear relationship model according to the charge and discharge depth to simulate the battery aging process; 3) Generate new energy power generation data, discharge power data, and electricity price data; 4) Design a new energy consumption processing strategy, including: 41) Adopt a segmented control method, a strategy of preferentially storing excess power in the energy storage system until it reaches the maximum capacity, to reduce the phenomenon of abandoned electricity; The specific segmented control method is: Within each time step, monitor the new energy power generation in real time and compare it with the current power demand; If the new energy power generation exceeds the current power demand, the excess power is preferentially stored in the energy storage system; during the power storage process, continuously track and update the state of charge of the energy storage system; When the state of charge of the energy storage system reaches the maximum capacity, if there is still excess power generation, encourage users to increase the electricity load to reduce the occurrence of abandoned electricity; 42) Energy storage arbitrage and economic evaluation: Design a dynamic programming model for solution, calculate the benefits obtained from charging or discharging at different time points according to the electricity price and the state of the energy storage system, and continuously adjust the charge and discharge strategy to maximize the profit of energy storage arbitrage.

2. The energy storage calculation method for new energy power generation and consumption as described in claim 1, wherein In step 1), the closed-loop process is a closed loop of power limitation - energy calculation - state of charge update - battery health state correction; including: The actual charging power does not exceed the maximum allowable power of the battery; Based on the charging efficiency, calculate the actual stored energy and convert it into a percentage of the state of charge. At the same time, subtract the battery health state proportionally according to the charging depth to simulate aging; Realize the discharge process through power constraint, energy accounting, state of charge monitoring, and battery health state correction: the discharge power does not exceed the maximum discharge capacity, accurately calculate the output energy based on the efficiency loss, ensure that the state of charge is not lower than the set minimum threshold, and dynamically update the battery health state according to the discharge depth to reflect the differential impact of different discharge depths on the battery life.

3. The energy storage calculation method for new energy power generation accommodation according to claim 1, wherein Specifically, the charging method in step 1) is as follows: When performing the charging operation, based on the input charging power, calculate the actually increased stored energy according to the charging efficiency, and synchronously update the state of charge, which is expressed as: ΔE charge = P charge × η charge × Δt Among them, ΔEcharge is the actually increased stored energy, P charge is the charging power, η charge is the charging efficiency, and Δt is the charging time interval; The discharging process specifically considers the influence of the discharging efficiency on the output energy and the corresponding change of the state of charge, and the calculation method is expressed as: ΔE discharge = P discharge × η discharge × Δt Among them, ΔE discharge is the actual output energy, P discharge is the discharge power, and η discharge is the discharge efficiency.

4. The energy storage calculation method for new energy power generation accommodation according to claim 1, wherein, In step 2), establish a linear aging relationship between the battery health state and the charge-discharge depth, including the charging aging coefficient and the discharging aging coefficient, simulate the battery aging process, and constrain the battery health state between 0 and 1.

5. The energy storage calculation method for new energy power generation and consumption according to claim 4, wherein, The battery health state update method includes: When the battery is charging, the method for updating the battery health state is expressed as: ΔSOH charge =-kcharge×ΔDOD charge Among them, ΔSOH charge is the change in the battery health state caused by charging, kcharge is the charging aging coefficient, and ΔDOD charge is the change in the depth of discharge; When the battery is discharging, the method for updating the battery health state is expressed as: ΔSOH discharge = -kdischarge×ΔDOD discharge , Among them, ΔSOH discharge is the change in the battery health state caused by discharge, kdischarge is the discharge aging coefficient, and ΔDOD discharge is the change in the depth of discharge.

6. The energy storage calculation method for new energy power generation and consumption according to claim 1, wherein In step 3), a hybrid model combining a sine function and random noise is used to generate new energy power generation data; a power price data generation function is used, and peak, flat, and valley periods are divided to generate power price data.

7. The energy storage calculation method for new energy power generation accommodation according to claim 1, wherein In step 41), a three-level priority control consumption strategy is adopted, specifically: When there is an excess of power generation, the electricity is preferentially stored in the energy storage until the state of charge reaches 95%, and a 5% safety capacity is reserved. When the energy storage system reaches the upper limit of the 95% state of charge, users are guided to increase the power consumption load through dynamic power prices. If there is still remaining power, cross-regional consumption is carried out.

8. The energy storage calculation method for new energy power generation and consumption as described in claim 1, characterized in that, In step 41), the optimization of the energy storage arbitrage strategy is achieved by designing a dynamic programming model, specifically including: First, the time is divided into Δt time intervals, and the state at each time step t is composed of the state of charge s t , the state of health of the battery h t , and the electricity price prediction p t . The goal of the dynamic programming model is to maximize the total profit, and the total profit Profit function is expressed as: Among them, T represents the total number of time steps after dividing a day at intervals of Δt; is the discharging electricity price at time step t, is the discharging electricity quantity at time step t, is the charging electricity price at time step t, is the charging electricity quantity at time step t, and η charge and η discharge respectively represent the charging efficiency and the discharging efficiency; C aging is the battery aging cost, which is calculated through the aging coefficient; and are the charging electricity quantity and the discharging electricity quantity at time step t; The new energy consumption processing process is coordinated with the energy storage system modeling and new energy power generation data, and the excess power, that is, the curtailed power, is obtained through the following calculation: E surplus = max(0, P gen - P demand ) × Δt Among them, E surplus represents the surplus power, and its value is jointly determined by the new energy power generation P gen and the current power demand P demand and the time step Δt; max(0, P gen - P demand ) ensures that the surplus power is calculated only when the power generation is greater than the demand power; if the power generation is less than or equal to the demand power, the surplus power is 0.

9. The energy storage calculation method for new energy power generation and consumption as claimed in claim 8, wherein The strategy optimization process is the iteration of the value function: Based on the Bellman equation, calculate the optimal benefit in each state for each time period, which is expressed as: where γ is the discount factor, R t is the immediate profit at the current time step, a t is the power of the charge and discharge action, including charging power, discharging power or idle; V t (s t , h t ) is the optimal value function at time step t with state s t ; V t+1 (s t+1 , h t+1 ) is the optimal value function at time step t + 1 and the updated state; s t+1 and h t+1 are the state of charge and the battery health state at time step t + 1, respectively; Update the state according to the state transition equation, and then calculate the optimal value in different states.

10. The energy storage calculation method for new energy power generation and consumption according to claim 9, characterized in that, The state transition equation is expressed as: Among them, capacity is the capacity of the energy storage system; and are the charging power and discharging power at time step t; η charge and η discharge represent the charging efficiency and discharging efficiency respectively.

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